A novel neural network architecture is proposed that provides a unified framework for Hebbian and backpropagation-based learning. The learning rule for this architecture, called the hybrid learning rule, combines the features of the Hebbian learning rule, which is a good feature extractor, and the backpropagation algorithm, which is an excellent classifier. By combining these two learning rules into the hybrid learning rule, the hybrid learning rule should have the strengths of both without any of the weaknesses. The hybrid learning rule was applied to the problem of isolated character recognition. While the hybrid learning rule failed to perform better than the backpropagation algorithm, it did generate receptive fields similar to those found by R. Linsker (1986), T.D. Sanger (1989), and D.H. Hubel and T.N. Wiesel (1962)